基于遥感与作物模型的土壤速效养分时空变异分析

doi:10.3864/j.issn.0578-1752.2019.03.008

Abstract

Abstract:

【Objective】 The spatio-temporal variability of soil available nutrients in Shuangshan farm during 2012-2016 was studied under uniform fertilization mode. The influence of human factors (such as fertilization management and crop rotation model) and natural factors (such as meteorology and topography) on the changes of available nutrients were analyzed, which could be used to provide references for soil available nutrients management and crop variable fertilization.【Method】 The time series satellite HJ-1 CCD imagery and the WOFOST crop model were selected to retrieve soil available nutrients. Based on the above soil available nutrients data, spatio-temporal variability of soil available nutrients analysis of available nutrients on different time scales (interannual and within-year) and different spatial scales (farm scale and field scale) was carried out. The hierarchical maps of soil available nutrients were used to qualitatively analyze the spatial variability of nutrients in five years. The specific statistical parameters were used to quantitatively analyze the average content and variability of soil available nutrients. The linear regression was used to analyze the relationship of variation and initial content of available nutrients. And the temporal variation characteristics of available nutrients were analyzed by the change curve of available nutrients. 【Result】 The average contents of available nitrogen (AN), available phosphorus (AP) and available potassium (AK) in the farm were not changed obviously from 2012 to 2016. The high and low values of available nutrients were all close to the middle value, which were concentrated at 280-360 mg·kg ^-1, 38-42 mg·kg ^-1and 160-200 mg·kg ^-1, respectively. And the area of these three parts increased by 18.5%, 23.1% and 23.8%, respectively, which showed uniform characteristics as a whole. The coefficient of variation of AN, AP, and AK changed from 0.314, 0.112, and 0.257 in 2012 to 0.131, 0.034, and 0.098 in 2016, respectively. And the spatial heterogeneity of available nutrients distributions was weakened. The variation of AN and AP were negatively correlated with the initial values in 2012, and the determination coefficient were 0.839 and 0.882, respectively. And the determination coefficient of AK was 0.569, its weak correlation was related to the instability of AK in the soil itself. The nutrients change characteristics of adjacent field indicated that the soil available nutrients on the field scales also showed obvious uniform characteristics. Crop rotation mode was the main factor affecting the difference of nutrients change between fields. For the field #1 and field #2 with the opposite rotation mode, artificial fertilization determined the general trend of nutrients change in the field. For leguminous plants, nitrogen fixation had a significant effect on the content of soil available nitrogen. The increase in temperature would promote the absorption of nutrients by crops to a certain extent, but this effect was not enough to change the general trend of nutrient changes. Soil leaching played a decisive role in the spatial variability of nutrients, especially for stronger precipitation. The effect of precipitation on nutrients spatial variability was more pronounced in areas with large topography variation.【Conclusion】 Farmland fertilization management and crop rotations were the dominant factors in the change of soil available nutrients, followed by topographic and climatic factors. The leaching of rainwater would lead to the loss and decline of soil available nutrients, which was more obvious in the area with larger topography. The amount of change in available nutrients was significantly related to the initial nutrients content. The increase of temperature would accelerate the decrease of available nutrients in soil, and its influence was less than that of precipitation. The above rules could be included in the prediction model of available nutrients, and different weights were assigned to these influencing factors to construct the prediction model, to achieve real-time dynamic monitoring of the variation of soil available nutrients in the inter-annual and growing seasons.

Key words: soil available nutrient, spatial-temporal variation, WOFOST crop model, uniform fertilization, correlation analysis

FANG HuiTing,MENG JiHua,CHENG ZhiQiang. Spatio-Temporal Variability of Soil Available Nutrients Based on Remote Sensing and Crop Model[J].Scientia Agricultura Sinica, 2019, 52(3): 478-490.

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References 36

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传感器 Sensor	采集时间 Time	轨道号 Orbit number
HJ-1A CCD1	2014-5-03	451-52
HJ-1A CCD2	2014-6-06	448-56
HJ-1A CCD1	2014-6-23	453-53
HJ-1A CCD1	2014-8-06	451-56
HJ-1B CCD2	2014-9-06	448-56
HJ-1A CCD1	2014-9-18	454-53
HJ-1A CCD2	2014-9-24	452-56
HJ-1B CCD2	2014-10-04	452-56

年份 Year	平均值 Mean (mg·kg^-1)	最小值 Min (mg·kg^-1)	最大值 Max (mg·kg^-1)	标准差 SD (mg·kg^-1)	变异系数 CV
2012	297.2	96.5	466.8	149.43	0.47
2013	304.9	170.1	457.9	93.21	0.30
2014	298.0	176.1	568.6	155.31	0.49
2015	301.9	187.2	397.3	75.30	0.24
2016	304.2	179.8	373.8	81.27	0.26

年度 Year	指标 Item	1 Jan.	2 Feb.	3 Mar.	4 Apr.	5 May	6 Jun.	7 Jul.	8 Aug.	9 Sep.	10 Oct.	11 Nov.	12 Dec.	平均值 Mean	总和 Sum
2012	平均温度 Average temperature (℃)	-28.8	-21.1	-8.9	4.5	13.8	20	22.3	19.1	12.9	2.7	-11.2	-24.5	0	/
2012	总降水量 Total precipitation (mm)	0.9	1.5	1.5	12.9	25.3	140.1	88.6	26.3	175.2	36.3	14.7	10.2	/	533.5
2013	平均温度 Average temperature (℃)	-27.6	-22	-12.3	2	14.9	19.4	21.4	19.1	12.3	3	-5.6	-17.8	0.6	/
2013	总降水量 Total precipitation (mm)	6	9.8	12.4	13.9	93.8	109.8	228.1	104.8	51.5	39.1	12.4	5.4	/	687
2014	平均温度 Average temperature (℃)	-24.6	-23.7	-7.1	8.2	12.5	21.5	20.5	19.6	12.1	2.2	-8.8	-20.9	1	/
2014	总降水量 Total precipitation (mm)	7.6	12.5	0	0.6	87.5	45.3	133.8	135.8	92.4	18.5	16.5	6.8	/	557.3
2015	平均温度 Average temperature (℃)	-19.8	-14.4	-6.9	4.7	11.4	20	22.5	20.8	12.7	3.7	-9.6	-17.2	2.3	/
2015	总降水量 Total precipitation (mm)	0.1	22.6	7.3	16.1	37.4	60.3	14	106.1	72.4	25	1.3	6.9	/	369.5
2016	平均温度 Average temperature (℃)	-22.1	-18.3	-3.8	4.6	13.8	17.1	21.35	18.91	14.09	0.103	-16.8	-21.46	0.6	/
2016	总降水量 Total precipitation (mm)	1.2	0.2	4	12	54.7	111.5	58.2	57.6	158.1	17.8	26.8	3.4	/	505.5

Spatio-Temporal Variability of Soil Available Nutrients Based on Remote Sensing and Crop Model

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年度 Year	中雨 Moderate rain (10-25 mm)	大雨 Heavy rain (25-50 mm)	暴雨 Torrential rain (50-100 mm)	总计 Total
2012	9	3	1	13
2013	20	5	0	25
2014	13	2	1	16
2015	10	2	0	12
2016	9	2	1	12